John Byabazaire is a Research Fellow at the School of Computer Science, University College Dublin (UCD). He holds a PhD in Computer Science from UCD (2024), following a BSc (Gulu University, 2013) and MSc (Waterford Institute of Technology, 2018). His research focuses on IoT systems for data collection, remote sensing, AI-driven end-to-end system management, and fog analytics. He has held academic roles including Assistant Lecturer at Gulu University (2018–2019) and teaching roles at UCD since 2019, including Occasional Lecturer and Senior Teaching Assistant. His research spans smart agriculture, data quality in IoT, and education technology. Notable contributions include frameworks for yield mapping in precision agriculture, trust-based data validation in IoT, and machine learning approaches for livestock health monitoring. He has secured grants like the National ICT Initiatives Support Program (Uganda Government, 2019–2020). Teaching includes courses on cloud computing, web development, and distributed systems. His articles emphasize IoT data quality, agricultural analytics, and educational technology innovation. He actively promotes technology adoption in African education and agriculture sectors through collaborative projects.
Dr. Stig Hellebust is a Lecturer in Physical Chemistry at the School of Chemistry, University College Cork (UCC), Ireland. Based in Room 206B of the Kane Building, he can be contacted at s.hellebust@ucc.ie or +353 214902680. His research focuses on atmospheric chemistry, environmental monitoring, and advanced data analysis techniques for understanding air quality and pollution sources across Ireland. Dr. Hellebust's research interests span several key areas of environmental chemistry and data science: Atmospheric observational data analysis, particularly high-dimensional datasets collected over extended time periods Application of multivariate statistical methods and machine learning for environmental data interpretation Source apportionment of atmospheric pollutants using receptor modeling techniques Development of algorithms for processing large environmental datasets Application of clustering and classification techniques to identify pollution sources Fourier-transform infrared spectroscopy data analysis His extensive publication record demonstrates expertise in air quality monitoring, particularly focusing on PM2.5 sources, urban pollution dynamics, and health impacts. He frequently employs advanced statistical methods including principal component analysis (PCA), positive matrix factorization (PMF), and various machine learning approaches to extract meaningful information from complex environmental datasets. His work bridges atmospheric science, public health, and data analytics, with significant contributions to understanding Ireland's air quality challenges. Dr. Hellebust has secured substantial research funding from multiple sources including the Environmental Protection Agency (EPA), Health Research Board, Science Foundation Ireland, and European Union programs. His current major projects include "Sources of PM2.5 in the Air of Irish Towns" (2024-2027, €233,796.00) and "Impact of Agricultural Emissions on Rural and Urban Air Quality" (2022-2025, €119,700.00), demonstrating his leadership in addressing critical environmental challenges. He currently supervises doctoral student Rósín Eileen Byrne and has previously supervised Eimear Heffernan who completed her PhD in 2022 on "Spatial and temporal variation of ambient carbonaceous aerosol in Ireland and strategies for effective monitoring of source contributions." His mentorship extends to interdisciplinary research connecting chemistry, environmental science, and public health. Dr. Hellebust is an active member of UCC's Atmospheric and Environmental Chemistry research group, collaborating with colleagues across Ireland and internationally on air quality monitoring and pollution source identification projects. His work has significant policy implications for urban planning, public health interventions, and environmental regulation in Ireland and beyond.
Dr. Dan Grigoras is a Senior Lecturer at the School of Computer Science and Information Technology, University College Cork. He holds a PhD from Politehnica University, Bucharest, and has industry experience as a computer systems engineer. Dr. Grigoras founded the Mobile and Cluster Computing Group in 2003 and initiated the MSc programme in Software and Systems for Mobile Networks, which he coordinated until 2011. His research focuses on mobile cloud computing, ad-hoc networks, middleware, and smart city applications. Key contributions include the development of context-aware middleware systems and cloud-managed MANETs. His work integrates mobile devices, cloud resources, and IoT infrastructure to enhance user experiences in dynamic environments. Dr. Grigoras' publications emphasize mobile cloud architectures, drone-based services, and emergency response systems. Trends show a shift from theoretical distributed computing to practical applications in real-time data processing, geo-analytics, and scalable cloud frameworks. Best Paper Award, CloudTech 2018 Cloud Challenge Award, IEEE/ACM 2015 He advises PhD and MSc students and has secured grants including €72,000 from IRCSET for mobile cloud user experience research and €16,244 for healthcare mobile cloud enterprises. His lab focuses on crowdsensing for smart cities and mobile cloud middleware.
Dr. Mohit Taneja is an Assistant Lecturer at South East Technological University and former Postdoctoral Research Fellow at Walton Institute. His research develops distributed computing solutions for IoT systems. Taneja's work focuses on fog computing architectures for latency-sensitive applications, particularly in agricultural technology. His EU-funded projects implement IoT solutions for smart dairy farming, including animal welfare monitoring and climate-neutral practices. Recent publications address network function virtualization management and blockchain transaction analysis. His technical innovations include machine learning frameworks for SLA compliance and resource optimization in edge computing environments.
Rudi Villing is an Associate Professor and Programme Director for Robotics & Intelligent Devices at Maynooth University's Department of Electronic Engineering, Faculty of Science & Engineering. He is actively affiliated with the Hamilton Institute and the Assisting Living and Learning (ALL) Institute at Maynooth University. Dr. Villing holds a first class honours B.Eng. in Electronic Engineering from Dublin City University and a PhD in Engineering from NUI Maynooth. Prior to his academic career, he spent 10 years working in the telecommunications software industry, specializing in Telecommunications Management Networks and software systems architecture. His primary research focuses on autonomous mobile robotics , systems for health and wellbeing , and applications of real-time intelligent systems . With expertise spanning system design, real-time embedded software, machine learning, autonomous behavior, signal processing, communications, and psychoperception, his work bridges theoretical research with practical applications. His recent publications demonstrate strong activity in robot vision, assistive robotics for elderly care, and computational healthcare applications, particularly in Parkinson's disease rehabilitation through the BeatHealth project. Dr. Villing's scientific contributions have been supported by funding from: Science Foundation Ireland Enterprise Ireland Irish Research Council European Commission As Programme Director, he plays a key leadership role in robotics education while maintaining an active research program. His work consistently translates theoretical concepts into practical implementations, particularly evident in his research on gait rehabilitation systems and quality control applications in food engineering. His recent publications show increasing interdisciplinary work at the intersection of robotics, healthcare, and food science. Dr. Villing is deeply involved with the Hamilton Institute and the ALL Institute, contributing to interdisciplinary research initiatives focused on intelligent systems with real-world impact. His laboratory work emphasizes practical robotics applications that address tangible human needs, especially in healthcare contexts where technology can improve quality of life for vulnerable populations.
Prof. Gabriel-Miro Muntean is a Professor at the School of Electronic Engineering, Dublin City University (DCU), Ireland. He holds a Ph.D. (2003) from DCU and B.Eng./M.Sc. degrees in Software Engineering from Politehnica University of Timisoara, Romania. He co-directs the DCU Performance Engineering Laboratory and is a Principal Investigator with Insight and Lero National Research Centres. His research focuses on multimedia networking, wireless/energy-aware communications, and technology-enhanced learning. Education: B.Eng. Software Engineering (Politehnica University of Timisoara, 1996) M.Sc. Software Engineering (Politehnica University of Timisoara, 1997) Ph.D. Electronic Engineering (DCU, 2003) Research Interests: Quality-oriented adaptive multimedia streaming Energy-efficient networking Personalized learning technologies 5G/6G network architectures Edge computing optimization Publications & Grants: Over 450 papers, 4 books, and 26 book chapters (H-index=53) EU Horizon 2020 projects: Coordinator of NEWTON, Lead in TRACTION Awards: IEEE Fellow (202X) IEEE Broadcast Technology Society Fellow Advising & Labs: Supervised 25 PhD students and 15 postdocs Co-director of DCU Performance Engineering Lab
Elias Tragos is a Research Fellow at the Insight Centre for Data Analytics, affiliated with University College Dublin (UCD), Ireland. His expertise spans wireless and mobile communications, cognitive radios, network architectures, fog computing, and security/privacy domains. PhD in Wireless Communications Master’s in Business Administration (MBA) in Techno-Economics Dr. Tragos has led or participated in numerous EU and national research projects, serving as researcher, Technical Manager, and Project Coordinator. His work has resulted in over 70 peer-reviewed publications with 1500+ citations (h-index 18).
